Parametric 3D models have enabled a wide variety of tasks in computer graphics and vision, such as modeling human bodies, faces, and hands. However, the construction of these parametric models is often tedious, as it requires heavy manual tweaking, and they struggle to represent additional complexity and details such as wrinkles or clothing. To this end, we propose Neural Parametric Models (NPMs), a novel, learned alternative to traditional, parametric 3D models, which does not require hand-crafted, object-specific constraints. In particular, we learn to disentangle 4D dynamics into latent-space representations of shape and pose, leveraging the flexibility of recent developments in learned implicit functions. Crucially, once learned, our neural parametric models of shape and pose enable optimization over the learned spaces to fit to new observations, similar to the fitting of a traditional parametric model, e.g., SMPL. This enables NPMs to achieve a significantly more accurate and detailed representation of observed deformable sequences. We show that NPMs improve notably over both parametric and non-parametric state of the art in reconstruction and tracking of monocular depth sequences of clothed humans and hands. Latent-space interpolation as well as shape/pose transfer experiments further demonstrate the usefulness of NPMs. Code is publicly available at https://pablopalafox.github.io/npms.

翻译：3D模型使计算机图形和视觉任务多种多样,例如模拟人体、脸部和手部。然而,这些参数模型的构建往往乏味,因为需要大量手工调整,难以代表更多复杂和细节,如皱纹或服装。为此,我们提出神经参数模型(NPMs),这是传统3D模型的一种新颖的、学习的替代方法,不需要手工制作的、目标特定限制,特别是,我们学会将4D动态分解为形状和形状的潜空表示,利用最近所学的隐含功能发展的灵活性。非常典型的是,我们的神经参数模型一旦学会了,能够对学习的空间进行优化,以适应新的观察,类似于传统参数模型(例如SMPL.)的安装。这使得国家预防机制能够对观察到的不易变形序列作出更准确和详细的描述。我们表明,国家预防机制在可观察的形状和形状的隐形空间中显著地改进了对准和非对等状态的描述,利用了最近所学的隐含功能发展的灵活性。我们的神经参数参数参数模型模型模型一旦学会过后,就能够优化利用新观察空间的系统化系统。